For many tasks the scientific literature needs to be checked systematically. The current practice is that scholars and practitioners screen thousands of studies by hand to find which studies to include in their review. This is error prone and inefficient. We therefore developed an open source machine learning (ML)-aided pipeline: Active learning for Systematic Reviews (ASReview). We show that by using active learning, ASReview can lead to far more efficient reviewing than manual reviewing, while exhibiting adequate quality. Furthermore, the presented software is fully transparent and open source.
van de Schoot, R., Bruin, J.D., Schram, R., Zahedi, P., Boer, J.D., Weijdema, F., Kramer, B., Huijts, M., Hoogerwerf, M., Ferdinands, G., Harkema, A., Willemsen, J., Ma, Y., Fang, Q., Hindriks, S. Tummers, L., & Oberski, D. (2020). An open source machine learning framework for efficient and transparent systematic reviews. Nat Mach Intell (2021). DOI: 10.1038/s42256-020-00287-7